Construction and validation of a nomogram for predicting chronic obstructive pulmonary disease with bronchiectasis

Chronic obstructive pulmonary disease (COPD) coexisting with bronchiectasis (BE) leads to increased symptom severity, elevated mortality rates, and worsened clinical outcomes. This study aimed to determine the independent risk factors (RFs) associated with COPD combined with BE (COPD-BE), subsequently establishing and validating a nomogram-based clinical prediction model. This model aims to early identify the presence of BE in patients with COPD, so that clinicians can quickly identify COPD-BE patients and formulate targeted management strategies to improve their prognosis and reduce mortality.

A total of 382 COPD patients were retrospectively enrolled and analyzed. Participants were randomly allocated at a 7:3 proportion into a training group comprising 268 cases and a validation group containing 114 cases. Subsequently, individuals were categorized based on whether BE was present, forming COPD-BE and COPD-alone subgroups. To identify RFs independently associated with COPD-BE, initial univariate logistic regression was performed, followed by least absolute shrinkage and selection operator (LASSO) regression for variable selection, and ultimately multivariable regression modeling. Using factors determined by the multivariate analysis, a predictive nomogram was subsequently developed. Receiver operating characteristic (ROC) analyses were conducted, and corresponding areas under the curve (AUCs) were calculated, to evaluate the predictive accuracy of the model. The nomogram's clinical effectiveness and accuracy were further validated through calibration assessments and decision curve analysis (DCA).

Independent RFs for COPD-BE included female sex, hemoptysis, history of pulmonary tuberculosis, Pseudomonas aeruginosa infection, globulin levels, and mechanical ventilation duration (P<0.05). The predictive model demonstrated good discriminative ability, with an AUC of 0.840 [95% confidence interval (CI): 0.790-0.890] in the training set and 0.829 [95% CI: 0.749-0.909] in the validation set. Calibration analyses showed good agreement between predicted probabilities and actual outcomes (P>0.05). Furthermore, DCA suggested the nomogram has potential clinical utility.

Female sex, hemoptysis, pulmonary tuberculosis history, Pseudomonas aeruginosa infection, globulin level, and mechanical ventilation duration are independent RFs for COPD-BE. The predictive model developed based on these factors demonstrated good predictive performance in internal validation. Pending further external validation, this nomogram holds promise as a useful tool to aid in the early identification of COPD-BE in clinical practice.